Course DescriptionThe toolkit of modern Data Scientists contains a large variety of instruments, both generic and specialized. All these tools can be used to analyze and extract useful connections from digital data. The theory behind guarantees that the resulting patterns will reflect the existing correlations, however not causal relationships. At the same time, in many applications, the desired outcome is the cause-effect model. This course aims to discuss the conditions under which the correlation does imply causation and to present the research direction that studies these conditions in a general form – Causal Learning. We believe that the knowledge of the basics of Causal Learning is an indispensable element of a Data Science practitioner toolkit.
- Jupyter Notebooks
- Python programming
- Fundamental of statistics, probability, calculus.